Figure 1: The correlation of sufficiently expressed candidate biomarkers with 3 hour lactate levels. Genes needed to be above the negative control threshold in at least 8/10 samples to be considered. Dashed lines represent the 80% correlation threshold.
Figure 2: Heatmap with biomarker expression data scaled by gene/row and hierarchally clustered by sample/column. Functional assessment based on the 3 hour lactate levels is labeled above the columns. Data for the 7 biomarkers with sufficient expression and correlation with lactate were used from all 10 of the original liver samples.
Figure 3: Mean relative expression of the geneset correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.
##
## Call:
## lm(formula = LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + GSTA1 +
## GSTA2, data = mlm.data)
##
## Residuals:
## FV2 LV2 LV3 FV3 LV1 FN1 FN2 LN1
## -1.988045 -0.512516 -0.786381 -0.364826 2.926899 0.921534 -0.290795 -0.002211
## FN3 LN3
## -0.009832 0.106172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.402e+00 2.639e+00 -0.910 0.459
## EPHX1 -1.030e-04 1.590e-03 -0.065 0.954
## TKT 1.063e-02 3.030e-02 0.351 0.759
## GPX2 -8.466e-04 1.971e-03 -0.429 0.709
## JUN 1.716e-02 3.227e-02 0.532 0.648
## CYP2B6 -6.794e-05 3.519e-04 -0.193 0.865
## GSTA1 5.072e-05 1.724e-04 0.294 0.796
## GSTA2 6.202e-04 1.113e-03 0.557 0.634
##
## Residual standard error: 2.691 on 2 degrees of freedom
## Multiple R-squared: 0.9721, Adjusted R-squared: 0.8744
## F-statistic: 9.949 on 7 and 2 DF, p-value: 0.09434
Figure 4: Generating a Multiple Linear Regression Predictive Model and applying it to the observed expression levels. The model is generated by incorporating expression data from the 7 biomarker genes across the 10 original sample livers and calculating a functional relationship to the lactate levels observed in their corresponding sample. The resulting intercept and coefficients for individual gene expression levels can be used to predict lactate levels for a sample. The plot displays the relationship between the levels predicted by the model when gene expression levels observed in the original 10 sample livers are used and the actual observed levels for those livers. The dashed line represents a 1:1 relationship between obersved and predicted lactate levels.
Figure 5: Checking model conditions. These figures are used to check the quality of the model by testing specific statistical parameters that are required for reliable application.
Figure 6: The predicted lactate levels generated by the model were tested for correlation with the known donor metrics.
Figure 7: Incorporating the additional samples into the established model
Figure 8: Correlation between the models predicted lactate levels and donor metrics
Figure 9: Expression levels of the 23 putative biomarkers observed in all 17 samples correlated with 3 hour lactate levels. The black lines represent 80% correlation and the red represents 60% correlation
Figure 10: Heatmap with data scaled by gene/row and clustered by sample/column. Functional data and DCD/DBD status is labeled above the columns
Figure 11: Mean relative expression of the geneset using all samples correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.
Figure 12: PCA using scaled data for all 17 samples
Figure 13: PCA using scaled data for the original 10 samples only
##
## Call:
## lm(formula = all.LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 +
## GSTA1 + GSTA2, data = mlm.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4829 -2.0591 -0.6445 1.4872 8.9090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8797389 2.5375224 -0.347 0.7368
## EPHX1 -0.0019644 0.0009802 -2.004 0.0760 .
## TKT 0.0271152 0.0101518 2.671 0.0256 *
## GPX2 0.0001518 0.0011427 0.133 0.8973
## JUN 0.0008919 0.0029752 0.300 0.7712
## CYP2B6 0.0001317 0.0002912 0.452 0.6618
## GSTA1 0.0001865 0.0001693 1.101 0.2993
## GSTA2 0.0010973 0.0006848 1.602 0.1435
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.208 on 9 degrees of freedom
## Multiple R-squared: 0.82, Adjusted R-squared: 0.6799
## F-statistic: 5.856 on 7 and 9 DF, p-value: 0.008688
Figure 14: Generating a Multiple Linear Regression Predictive Model using all samples and applying it to the observed expression levels of all 17 samples
Figure 15: Checking the new model conditions
Figure 16: Correlation between the new models predicted lactate levels and donor metrics
## HSP90B1 TFAM ALB BCL2 COX6B1 COX17 MCL1
## 0.0853985 0.3698896 0.1814015 0.2063185 0.3290794 0.1212330 0.4920644
##
## Call:
## lm(formula = LacData ~ gene1 + gene2 + gene3 + gene4 + gene5 +
## gene6 + gene7, data = mlm.data)
##
## Residuals:
## FV2 LV2 LV3 FV3 LV1 FN1 FN2 LN1 FN3 LN3
## -2.6489 -9.0340 -2.3194 -3.4053 2.2324 -0.9814 0.8099 -1.9560 11.0976 6.2053
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.535e+00 2.416e+01 -0.188 0.868
## gene1 9.516e-04 4.406e-03 0.216 0.849
## gene2 1.457e-01 2.110e-01 0.690 0.561
## gene3 -1.658e-05 1.608e-04 -0.103 0.927
## gene4 -2.814e-01 4.838e-01 -0.582 0.620
## gene5 -1.146e-02 3.094e-02 -0.370 0.747
## gene6 2.330e-03 4.199e-02 0.055 0.961
## gene7 3.185e-02 6.646e-02 0.479 0.679
##
## Residual standard error: 11.78 on 2 degrees of freedom
## Multiple R-squared: 0.4646, Adjusted R-squared: -1.409
## F-statistic: 0.2479 on 7 and 2 DF, p-value: 0.9317
Figure 17: Generating a model using genes that do not correlate with lactate. Uses a random set of 7 genes from the nanostring data that have sufficient expression and a positive correlation value less than .5.
Figure 18: The correlation of sufficiently expressed genes detected in the nanostring custom panel with 3 hour lactate levels in DBD livers. Genes needed to be above the negative control threshold in at least 8/10 samples to be considered. Dashed lines represent the 80% correlation threshold.
Figure 19: The correlation of sufficiently expressed genes detected in the nanostring custom panel with 3 hour lactate levels in DBD and DCD livers. Genes needed to be above the negative control threshold in at least 8/10 samples to be considered. Dashed lines represent the 80% correlation threshold.
Figure 20: Showing only those with at least 80% correlation in the DBD livers. Note: NRF1 was not used in the original analysis of DCD livers since there was a low correlation between the Lactade data and RNAseq data.
Figure 21: Showing the fold change between adequate and low functioning livers for those genes with at least 80% correlation. Note: NRF1 was not used in the original analysis of DCD livers since there was a low correlation between the Lactade data and RNAseq data.
Figure 22: Showing the expression leves for those genes with at least 80% correlation.
Figure 22: Showing the expression leves for those genes with at least 80% correlation.
Figure 22: Showing the expression leves for those genes with at least 80% correlation.
##
## Call:
## lm(formula = all.LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 +
## GSTA1 + GSTA2 + fwit, data = mlm.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9600 -2.2272 -0.7964 1.6400 8.7994
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4960950 3.9506282 0.126 0.9032
## EPHX1 -0.0019193 0.0010300 -1.863 0.0994 .
## TKT 0.0293559 0.0116414 2.522 0.0357 *
## GPX2 -0.0001388 0.0013457 -0.103 0.9204
## JUN -0.0006743 0.0045585 -0.148 0.8861
## CYP2B6 0.0001509 0.0003074 0.491 0.6367
## GSTA1 0.0002121 0.0001853 1.144 0.2856
## GSTA2 0.0010787 0.0007176 1.503 0.1712
## fwit -0.1753180 0.3727847 -0.470 0.6507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.402 on 8 degrees of freedom
## Multiple R-squared: 0.8248, Adjusted R-squared: 0.6496
## F-statistic: 4.708 on 8 and 8 DF, p-value: 0.02103
Figure 23: Generating a model using the original biomarkers and incorporating wit.